# Pooled single-cell screen in colorectal cancer defines transcriptional modules linked to oncogenes

## TL;DR

This study uses single-cell screening to identify transcriptional modules linked to oncogenes in colorectal cancer, which can help predict patient outcomes and improve classification systems.

## Contribution

The study introduces a novel method to link oncogenic signaling to transcriptional states and clinical outcomes using a pooled single-cell transcriptomic screen.

## Key findings

- Ten conserved oncogene-driven transcriptional modules were identified, representing core cancer phenotypes.
- High activity in certain modules is associated with worse or better prognosis in CRC patient cohorts.
- Transcriptional modules improve existing CRC classification systems like CMS.

## Abstract

Oncogenic mutations shape colorectal cancer (CRC) biology, yet their impact on transcriptional phenotypes remains incompletely understood, and their individual prognostic value is limited. Here, we perform a pooled single-cell transcriptomic screen of over 100,000 CRC cells with a comprehensive barcoded library of oncogenic variants across genetically diverse CRC lines. Using a variational autoencoder-based interpretable factor model, we identify ten conserved oncogene-driven transcriptional modules (TMOs) representing core cancer phenotypes such as cellular plasticity, inflammatory response, replicative stress, and epithelial-to-mesenchymal transition. Engagement of these modules can be context-dependent, reflecting interactions between oncogene-induced driver pathways and background genetics. TMO activity in patient tumors stratifies CRC cohorts into high- and low-risk groups, improving relapse-free survival prediction beyond existing classification systems. Our study systematically links oncogenic signaling to transcriptional states and clinical outcomes, establishing a functional framework for module-based patient stratification in precision oncology.

Single-cell screening with a comprehensive barcoded library of oncogenic variants in a panel of CRC lines identifies conserved transcriptional modules representing cancer phenotypes. Several modules can stratify patient cohorts into high and low-risk groups.

An interpretable factor model identifies module signatures for core cancer traits.High activity of the Inflammatory Response, Plasticity/Drug Resistance, and EMT modules are associated with a worse prognosis in CRC patient cohorts.High Replicative Stress module activity is associated with a better prognosis in CRC patient cohorts.Transcriptional modules can improve CRC classification systems such as CMS.

An interpretable factor model identifies module signatures for core cancer traits.

High activity of the Inflammatory Response, Plasticity/Drug Resistance, and EMT modules are associated with a worse prognosis in CRC patient cohorts.

High Replicative Stress module activity is associated with a better prognosis in CRC patient cohorts.

Transcriptional modules can improve CRC classification systems such as CMS.

Single-cell screening with a comprehensive barcoded library of oncogenic variants in a panel of CRC lines identifies conserved transcriptional modules representing cancer phenotypes. Several modules can stratify patient cohorts into high and low-risk groups.